An experiment quantifying human sensitivity to supra-threshold compression artifacts in wavelet-compressed images allows a computation and comparison of mean-squared errors producing equivalent visual distortion, and demonstrates that common assumptions used in wavelet image compression do not necessarily hold for low-rate images. The resulting MSE in the image is constant for a spatial frequency and is independent of orientation. Because the quantizers are not operating in the granular region, however, equal MSE does not imply equal quantizer step sizes. Computed threshold elevations for the second and third visible degradations are predicted well by the constant MSE model. Supra-threshold distortions in complex stimuli do not therefore behave according to sub-threshold assumptions, which suggest that the quantizer step size should be constant for a spatial frequency.

A robust image transmission system incorporating perceptually-based coding and error protection is proposed for wireless channels. The perceptually-based image coder consists of a wavelet transformation, uniform quantization, and variable-length entropy encoding. The coded data is placed in a spatially scalable stream divided into segments of varying perceptual importance and tolerance to transmission errors. A novel unequal error protection system is designed using turbo codes for the wireless channel, neural networks to predict decoder error and calculate erasures, and outer turbo-code concatenation of Reed-Solomon codes at different rates. A model of error tolerance is developed to minimize the added redundancy required to meet the desired throughput performance and visual quality level. The proposed system provides excellent reliability allowing the transmission of high visual quality images at a small additional redundancy and low signal-to-noise ratio (4.5 dB).

With the proliferation of readily available image content, data hiding/watermarking has become a topic of considerable importance. In many applications, it is desirable to embed identifying data strings for authentication, or to embed contextual data that may be used for other purposes. It is also desirable that the image into which the data has been embedded be visually indistinguishable from the original. This paper presents a method in which psychovisual metrics are used in a wavelet based algorithm that can embed a set of bits into an image. The resulting image remains visually identical to the original. The algorithm can be used to embed very robust authentication data, or less robust data to be used for other purposes, where the robustness is a function of the number of bits to be embedded.

A psychovisually-based quantization strategy for wavelet coded images is proposed. The quantization strategy is based on the results of a psychovisual evaluation to quantify sensitivities to supra-threshold distortion caused by wavelet coefficient quantization in natural images. Quantizer step sizes to produce a minimum noticeable distortion are parameterized in terms of subband standard deviation and a quantization strategy is subsequently proposed. The resulting strategy is applied to both intraband and embedded wavelet coders and reveals a significant perceptual improvement when images exhibit supra-threshold distortion.

This paper presents a modified JPEG coder that is applied to the compression of mixed documents (containing text, natural images, and graphics) for printing purposes. The modified JPEG coder proposed in this paper takes advantage of the distinct perceptually significant regions in these documents to achieve higher perceptual quality than the standard JPEG coder. The region-adaptivity is performed via classified thresholding being totally compliant with the baseline standard. A computationally efficient classification algorithm is presented, and the improved performance of the classified JPEG coder is verified.

We propose a new technique for transform coding based on rate-distortion (RD) optimized thresholding (i.e. discarding) of wasteful coefficients. The novelty in this proposed algorithm is that the distortion measure is made adaptive. We apply the method to the compression of mixed documents (containing text, natural images, and graphics) using JPEG for printing. Although the human visual system's response to compression artifacts varies depending on the region, JPEG applies the same coding algorithm throughout the mixed document. This paper takes advantage of perceptual classification to improve the performance of the standard JPEG implementation via adaptive thresholding, while being compatible with the baseline standard. A computationally efficient classification algorithm is presented, and the improved performance of the classified JPEG coder is verified. Tests demonstrate the method's efficiency compared to regular JPEG and to JPEG using non-adaptive thresholding. The non-stationary nature of distortion perception is true for most signal classes and the same concept can be used elsewhere.

Psychophysical studies have shown that there are three image components of distinct perceptual significance to human observers: strong edges, smooth regions, and textured or detailed regions. A psychophysical test was performed to evaluate the perceptual role of each region and a segmentation algorithm was developed to segment an image into the three regions. The segmentation algorithm identifies blocks of an image as belonging to one of the perceptual regions by analyzing high frequency coefficients of a 3-level hierarchical subband/wavelet decomposition. The segmentation algorithm performs well on a wide range of image content, including natural images and mixed images containing text, graphics, and natural scenes.